{rfName}
De

Indexed in

License and Use

Icono OpenAccess

Citations

42

Altmetrics

Analysis of institutional authors

Ríos-Sánchez BCorresponding AuthorMartín-Yuste NAuthorSanchez-Avila CAuthor

Share

May 18, 2020
Publications
>
Article
Bronze

Deep learning for face recognition on mobile devices

Publicated to: IET Biometrics. 9 (3): 109-117 - 2020-05-01 9(3), DOI: 10.1049/iet-bmt.2019.0093

Authors:

Rios-Sanchez, Belen; Costa-da Silva, David; Martin-Yuste, Natalia; Sanchez-Avila, Carmen
[+]

Affiliations

Grupo de Biometría, Bioseñales, Seguridad y Smart Mobility. Universidad Politécnica de Madrid - Author
Univ Politecn Madrid, Grp Biometr Biosignals & Secur, Edif CeDInt UPM,Campus Montegancedo - Author
Universidad Politécnica de Madrid - Author
See more

Abstract

© The Institution of Engineering and Technology 2020. Mobility implies a great variability of capturing conditions, which is not easy to control and directly affects to face detection and the extraction of facial features. Deep learning solutions seem to be the most interesting choice for automatic face recognition, but they are highly dependent on the model generated during the training stage. In addition, the size of the models makes it difficult for their integration into applications oriented to mobile devices, particularly when the model must be embedded. In this work, a small-size deep-learning model was trained for face recognition on low capacity devices and evaluated in terms of accuracy, size and timings to provide quantitative data. This evaluation is aimed to cover as many scenarios as possible, so different databases were employed, including public and private datasets specifically oriented to recreate the complexity of mobile scenarios. Also, publicly available models and traditional approaches were included in the evaluation to carry out a fair comparison. Moreover, given the relevance of template matching and face detection stages, the assessment is complemented with different classifiers and detectors. Finally, a JAVA-Android implementation of the system was developed and evaluated to obtain performance data of the whole system integrated on a mobile phone.
[+]

Keywords

Automatic face recognitionCapturing conditionsClassificationDeep learning solutionsFace detection stagesFace recognitionFacial featuresGreat variabilityInteresting choiceJavaLearning (artificial intelligence)Low capacity devicesMobile computingMobile devicesMobile phoneMobile scenariosPrivate datasetsPublic datasetsPublicly available modelsSmall-size deep-learning modelTemplate matchingTraining stage

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IET Biometrics due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Signal Processing. Notably, the journal is positioned en el Cuartil Q3 for the agency WoS (JCR) in the category Computer Science, Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-07:

  • Google Scholar: 13
  • WoS: 12
  • Scopus: 16
[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-07:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 54.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 54 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/79163/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 260
  • Downloads: 76
[+]

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (RIOS SANCHEZ, BELEN) and Last Author (SANCHEZ AVILA, MARIA DEL CARMEN).

the author responsible for correspondence tasks has been RIOS SANCHEZ, BELEN.

[+]